Subarachnoid Hemorrhage from Intracranial Aneurysms

Abstract

Measuring and predicting the user’s Quality of Experience (QoE) of a multimedia stream is the first step towards improving and optimizing the provision of mobile streaming services. This enables us to better understand how Quality of Service (QoS) parameters affect service quality, as it is actually perceived by the end user. Over the last years this goal has been pursued by means of subjective tests and through the analysis of the \nuser’s feedback.\nExisting statistical techniques have lead to poor accuracy (order of 70%) and inability to evolve prediction models with the system’s dynamics. In this paper, we propose a novel approach for building accurate and adaptive QoE prediction models using Machine\nLearning classification algorithms, trained on subjective test data.\nThese models can be used for real-time prediction of QoE and can be efficiently integrated into online learning systems that can adapt the models according to changes in the environment.\nProviding high accuracy of above 90%, the classification algorithms become an indispensible component of a mobile multimedia QoE management system.